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Supernova Remnants As Clues to Their Progenitors

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Supernova Remnants As Clues to Their Progenitors Supernova Remnants as Clues to Their Progenitors Daniel Patnaude and Carles Badenes Abstract Supernovae shape the interstellar medium, chemically enrich their host galaxies, and generate powerful interstellar shocks that drive future generations of star formation. The shock produced by a supernova event acts as a type of time ma- chine, probing the mass loss history of the progenitor system back to ages of ∼ 10 000 years before the explosion, whereas supernova remnants probe a much earlier stage of stellar evolution, interacting with material expelled during the progenitor’s much earlier evolution. In this chapter we will review how observations of super- nova remnants allow us to infer fundamental properties of the progenitor system. We will provide detailed examples of how bulk characteristics of a remnant, such as its chemical composition and dynamics, allow us to infer properties of the progeni- tor evolution. In the latter half of this chapter, we will show how this exercise may be extended from individual objects to SNR as classes of objects, and how there are clear bifurcations in the dynamics and spectral characteristics of core collapse and thermonuclear supernova remnants. We will finish the chapter by touching on re- cent advances in the modeling of massive stars, and the implications for observable properties of supernovae and their remnants. 1 Introduction Supernova remnants provide a unique view of the supernova (SN) phenomenon. The hundreds of extragalactic supernovae (SNe) discovered each year by modern Daniel Patnaude Smithsonian Astrophysical Observatory, 60 Garden St, Cambridge, MA 02138, e-mail: arXiv:1702.03228v1 [astro-ph.HE] 10 Feb 2017 [email protected] Carles Badenes Department of Physics and Astronomy, and Pittsburgh Particle Physics, Astrophysics, and Cosmol- ogy Center (PITT-PACC), University of Pittsburgh, 3931 O’Hara St, Pittsburgh, PA 15260 e-mail: [email protected] 1 2 Daniel Patnaude and Carles Badenes surveys are unresolved point sources which fade before they can interact with any circumstellar material outside of their most immediate surroundings. Before they fade, they probe the evolution of the progenitor on timescales of ∼ 100–1000 years before the explosion. This is because the typical outflow speeds from the progenitor are ∼ 10–1000 km s−1, 100-1000× slower than the typical supernova blastwave velocity. In contrast, supernova remnants (SNRs) in the Local Group galaxies can be re- solved in some detail as they interact with the bulk of the circumstellar material expelled by their progenitors during pre-SN mass loss over much longer timescales. Additionally, SNRs are surrounded by resolved stellar populations that spawned their SN progenitors (Badenes et al., 2009; Williams et al., 2014; Jennings et al., 2014), and they have become prime hunting grounds in the search for their surviv- ing binary companions (e.g., Edwards et al., 2012; Kerzendorf et al., 2014). X-ray spectra of SNRs can reveal emission from neutron-rich isotopes in the ejecta which are sensitive probes of SN explosion mechanisms (e.g., Yamaguchi et al., 2015). In short, SNR observations can constrain aspects of SNe and their progenitors that are either difficult or impossible to access by optical SN observations. In this chapter, we review the clues that supernova remnants provide about their progenitor’s evolution and ultimate fate. We begin by highlighting studies of both Type Ia and core-collapse SNRs which inferred properties of their progenitors and evolution. We then discuss recent techniques that use bulk SNR observables to con- strain SN progenitor properties. We conclude by touching on recent results in mas- sive star evolution which may be relevant towards understanding observable prop- erties in supernova remnants. For further background and a different perspective of these topics, see the reviews by Badenes (2010) and Vink (2012). 2 Supernova Remnant - Progenitor Connections The main challenge associated with studying supernova remnants is related to their type. Classically, SNe have been typed by their optical spectrum around maximum light (Filippenko, 1997). In general terms, Type Ia supernovae show no hydrogen in their optical spectra, with Type Ia (thermonuclear) SNe exhibiting strong Si II absorption around 615 nm. Type Ib/c are core-collapse supernovae, and differ from Ia SNe in that they possess either a weak or non-existent silicon absorption feature, and little (Type Ib) or no (Ic) absorption from He I. The amount of helium in the optical spectrum may provide insight into the degree of mass-loss in the progenitor prior to core-collapse. In contrast, Type II SNe do show hydrogen in their spectra, and are further subdivided into types IIP, IIL, IIb and IIn, based on the evolution and characteristics of their light curve or, in the case of Type IIn SNe, their optical spectrum. While SNe are common and somewhat accessible to typing, SNRs are evolved objects, and a priori it is non-trivial to associate them with each of the established SN types, though the detection of optical light echoes from the SN event do provide Supernova Remnants as Clues to Their Progenitors 3 clues in some cases (Rest et al., 2008). However, given the wealth of data available, a challenging opportunity exists to connect remnant properties back to the properties of the progenitor. Connecting remnant properties to those of the progenitor often involves a detailed analysis of the remnant’s broadband spectrum, combined with comparisons to hy- drodynamical models for supernova remnant ejecta evolution. In some cases such as those discussed below, this has produced interesting constraints. However, bulk properties such as emission line strengths, energy centroids, and chemical abun- dances depend on different aspects of a SN explosion than those of optical SNe. These properties can act as crucial benchmarks for specific stellar evolution sce- narios. In x 3 of this chapter, we explore how these so called “bulk observables” connect directly to the evolution of the progenitor, and how theorists and observers are connecting detailed stellar evolution models to properties of young supernova remnants. When studying SNRs, both as individual objects and as a class, the main proper- ties to consider are the chemical composition of the ejecta and the dynamics of the blast wave. The spectrum of a SNR in the X-ray band is a combination of nonthermal emission from the blast wave, and thermal emission from both swept-up circumstel- lar material and shocked supernova ejecta. The chemical composition of the shocked ejecta is determined by fitting models to the observed X-ray spectrum, which often requires some knowledge of the SNR dynamics (Borkowski et al., 2001). In turn, the blast wave dynamics are inferred by relying on critical assumptions about the re- lationship between the shock speed and the measured electron temperature. Only in certain cases, such as in the Galactic SNR Cas A, are the dynamics directly observed (DeLaney & Rudnick, 2003; Patnaude & Fesen, 2009). In most cases, the morphology is compared to self-similar models for blast wave evolution, which again require assumptions about the remant’s age, energetics, and circumstellar environment. These methods are vulnerable to circular logic and can result in detailed statements about the properties of a given SNR that fail to properly account for the considerable uncertainties introduced by the models. In other words, the critical assumptions made in connecting spectral properties to dynamics often compromise the the connections made back to the progenitor evolution. As an example, specific stellar evolution scenarios from SN Ia progenitors pre- dict circumstellar structures that range from the r ∝ r−2 profiles that are the sig- nature of isotropic mass-loss to relatively large low-density energy-driven cavities excavated by fast outflows from accreting white dwarfs (Badenes et al., 2007; Shen et al., 2013). As seen in Figure 1, the prompt radio and X-ray emission from SNe (Chomiuk et al., 2016; Margutti et al., 2014), weeks or months after the explosion, can only probe circumstellar material (CSM) structures up to a few hundred AU, composed of material expelled by the progenitor hundreds to thousands of years before the explosion, depending on the outflow velocity. In contrast, SNRs that are hundreds or thousands of years old can probe CSM structures up to several pc in size, expelled by the progenitor several thousands to millions of years before the explosion. These spatial scales are representative of typical stellar wind blown bubbles (Koo & McKee, 1992), and the associated 4 Daniel Patnaude and Carles Badenes temporal scales are directly relevant to stellar evolution scenarios for Type Ia SN progenitors (Wang & Han, 2012). For CC SNRs, the argument is even stronger, as mass-loss is often the single most uncertain ingredient in stellar evolution models for SN progenitors (Smith, 2014). Fig. 1 Schematic circumstellar density profiles for isotropic SN Ia progenitor outflows (colored −1 3 −1 plots, dashed for uw = 20 km s ; solid for uw = 10 km s ), compared to the typical densities in the warm phase of the interstellar medium (horizontal grey band) (Ferriere,` 2001). The rulers on top of the plots show the time before the explosion (in yr) when the material at that radius was ejected by the SN progenitor. The rulers on the bottom show the typical sizes of circumstellar cavities excavated by fast outflows (Badenes et al., 2007; Shen et al., 2013). The radii probed by X-ray/radio follow-up observations of SNe and studies of Type Ia SNRs are shown by blue and red shaded vertical stripes. The blue arrows and red circles show upper limits for two Type Ia SNe (Chomiuk et al., 2016; Perez-Torres´ et al., 2014) and measurements for three Type Ia SNRs (Raymond et al., 2007; Slane et al., 2014; Borkowski et al., 2014). A different approach is instead of studying the minute details of individual ob- jects, one probes the general characteristics of SNRs to see how they relate to the expected characteristics of a model derived from a potential progenitor scenario.
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